Discrete Choice Modeling
William Greene
Stern School of Business
New York University
Lab Sessions
Lab Session 8
Discrete Choice, Multinomial
Logit Model
Observed Data

Types of Data






Individual choice
Market shares
Frequencies
Ranks
Attributes and Characteristics
Choice Settings


Cross section
Repeated measurement (panel data)
Data for Multinomial Choice
Line
1
2
3
4
5
6
7
8
321
322
323
324
325
326
327
328
MODE
AIR
TRAIN
BUS
CAR
AIR
TRAIN
BUS
CAR
AIR
TRAIN
BUS
CAR
AIR
TRAIN
BUS
CAR
TRAVEL
.00000
.00000
.00000
1.0000
.00000
.00000
.00000
1.0000
.00000
.00000
1.0000
.00000
.00000
.00000
.00000
1.0000
INVC
59.000
31.000
25.000
10.000
58.000
31.000
25.000
11.000
127.00
109.00
52.000
50.000
44.000
25.000
20.000
5.0000
INVT
100.00
372.00
417.00
180.00
68.000
354.00
399.00
255.00
193.00
888.00
1025.0
892.00
100.00
351.00
361.00
180.00
TTME
69.000
34.000
35.000
.00000
64.000
44.000
53.000
.00000
69.000
34.000
60.000
.00000
64.000
44.000
53.000
.00000
GC
70.000
71.000
70.000
30.000
68.000
84.000
85.000
50.000
148.00
205.00
163.00
147.00
59.000
78.000
75.000
32.000
HINC
35.000
35.000
35.000
35.000
30.000
30.000
30.000
30.000
60.000
60.000
60.000
60.000
70.000
70.000
70.000
70.000
Using NLOGIT To Fit the Model
Start program
Load CLOGIT.LPJ project
Use command builder dialog box
or
Use typed commands in editor
Specification of Choice Variable
Specification of Utility Functions
Copy the variable
names from the list
at the right into the
appropriate window
at the left, then
press Run
Submit Command from Editor
(1)
Type commands in editor
(2)
Highlight by dragging mouse
(3)
Press GO button
Command Structure
Generic
CLOGIT (or NLOGIT) ; Lhs = choice variable
; Choices = list of labels for the J choices
; RHS = list of attributes that vary by choice
; RH2 = list of attributes that do not vary by choice $
For this application
CLOGIT (or NLOGIT) ; Lhs = MODE
; Choices = Air, Train, Bus, Car
; RHS = TTME,INVC,INVT,GC
; RH2 = ONE, HINC $
Output
Window
Note: coef. on GC
has the wrong sign!
Effects of Changes in Attributes on Probabilities
Partial Effects: Effect of a change in attribute “k” of
alternative “m” on the probability that choice “j” will be
made is
Pj
xmk
= Pj [1(j = m) - Pm ]βk
Proportional changes: Elasticities
logPj
logx mk
x mk
=
Pj [1(j = m) - Pm ]βk
Pj
= [1(j = m) - Pm ]βk x mk
Note the elasticity is the same for all choices “j.” (IIA)
Elasticities for CLOGIT
Request: ;Effects: attribute (choices where changes )
; Effects: INVT(*) (INVT changes in all choices)
+---------------------------------------------------+
| Elasticity
averaged over observations.|
| Attribute is INVT
in choice AIR
|
| Effects on probabilities of all choices in model: |
| * = Direct Elasticity effect of the attribute.
|
|
Mean
St.Dev
|
| *
Choice=AIR
-1.3363
.7275
|
|
Choice=TRAIN
.5349
.6358
|
|
Choice=BUS
.5349
.6358
|
|
Choice=CAR
.5349
.6358
|
| Attribute is INVT
in choice TRAIN
|
|
Choice=AIR
2.2153
2.4366
|
| *
Choice=TRAIN
-6.2976
4.0280
|
|
Choice=BUS
2.2153
2.4366
|
|
Choice=CAR
2.2153
2.4366
|
| Attribute is INVT
in choice BUS
|
|
Choice=AIR
1.1942
1.7469
|
|
Choice=TRAIN
1.1942
1.7469
|
| *
Choice=BUS
-7.6150
3.4417
|
|
Choice=CAR
1.1942
1.7469
|
| Attribute is INVT
in choice CAR
|
|
Choice=AIR
2.0852
2.0953
|
|
Choice=TRAIN
2.0852
2.0953
|
|
Choice=BUS
2.0852
2.0953
|
| *
Choice=CAR
-5.9367
3.7493
|
+---------------------------------------------------+
Own effect
Cross effects
Note the effect of
IIA on the cross
effects.
Other Useful Options
; Describe for descriptive by statistics, by
alternative
; Crosstab for crosstabulations of actuals
and predicted
; List for listing of outcomes and predictions
; Prob = name to create a new variable with
fitted probabilities
; IVB = log sum, inclusive value. New variable
Analyzing Behavior of Market Shares
Scenario: What happens to the number of people how make
specific choices if a particular attribute changes in a
specified way?
Fit the model first, then using the identical model setup, add
; Simulation = list of choices to be analyzed
; Scenario = Attribute (in choices) = type of change
For the CLOGIT application, for example
; Simulation = * ? This is ALL choices
; Scenario: INVC(car)=[*]1.25$ INVC rises by 25%
More Complicated Model Simulation
In vehicle cost of CAR rises by 25%
Market is limited to ground (Train, Bus, Car)
NLOGIT
; Lhs = Mode
; Choices = Air,Train,Bus,Car
; Rhs = TTME,INVC,INVT,GC
; Rh2 = One ,Hinc
; Simulation = TRAIN,BUS,CAR
; Scenario: INVC(car)=[*]1.25$
Model Simulation
In vehicle cost of CAR rises by 25%
+------------------------------------------------------+
|Simulations of Probability Model
|
|Model: Discrete Choice (One Level) Model
|
|Simulated choice set may be a subset of the choices. |
|Number of individuals is the probability times the
|
|number of observations in the simulated sample.
|
|Column totals may be affected by rounding error.
|
|The model used was simulated with
210 observations.|
+------------------------------------------------------+
------------------------------------------------------------------------Specification of scenario 1 is:
Attribute Alternatives affected
Change type
Value
--------- ------------------------------- ------------------- --------INVC
CAR
Scale base by value
1.250
------------------------------------------------------------------------The simulator located
209 observations for this scenario.
Simulated Probabilities (shares) for this scenario:
+----------+--------------+--------------+------------------+
|Choice
|
Base
|
Scenario
| Scenario - Base |
Changes in the
|
|%Share Number |%Share Number |ChgShare ChgNumber|
predicted market
+----------+--------------+--------------+------------------+
shares when
|TRAIN
| 37.321
78 | 40.711
85 | 3.390%
7 |
|BUS
| 19.805
42 | 22.560
47 | 2.755%
5 |
INVC_CAR changes
|CAR
| 42.874
90 | 36.729
77 | -6.145%
-13 |
|Total
|100.000
210 |100.000
209 |
.000%
-1 |
+----------+--------------+--------------+------------------+
Compound Scenario: INVC(Car) falls by 10%,
TTME (Air,Train) rises by 25%
(at the same time).
+------------------------------------------------------+
|Simulations of Probability Model
|
|Model: Discrete Choice (One Level) Model
|
|Simulated choice set may be a subset of the choices. | ;simulation=*
|Number of individuals is the probability times the
| ; scenario: INVC(car)=[*]0.9 /
|number of observations in the simulated sample.
|
TTME(air,train)=[*]1.25
|Column totals may be affected by rounding error.
|
|The model used was simulated with
210 observations.|
+------------------------------------------------------+
------------------------------------------------------------------------Specification of scenario 1 is:
Attribute Alternatives affected
Change type
Value
--------- ------------------------------- ------------------- --------INVC
CAR
Scale base by value
.900
TTME
AIR
TRAIN
Scale base by value
1.250
------------------------------------------------------------------------The simulator located
209 observations for this scenario.
Simulated Probabilities (shares) for this scenario:
+----------+--------------+--------------+------------------+
|Choice
|
Base
|
Scenario
| Scenario - Base |
|
|%Share Number |%Share Number |ChgShare ChgNumber|
+----------+--------------+--------------+------------------+
|AIR
| 27.619
58 | 16.516
35 |-11.103%
-23 |
|TRAIN
| 30.000
63 | 23.012
48 | -6.988%
-15 |
|BUS
| 14.286
30 | 18.495
39 | 4.209%
9 |
|CAR
| 28.095
59 | 41.977
88 | 13.882%
29 |
|Total
|100.000
210 |100.000
210 |
.000%
0 |
+----------+--------------+--------------+------------------+
Choice Based Sampling
Over/Underrepresenting alternatives in the data set
Choice
Air
Train
Bus
Car
True
0.14
0.13
0.09
0.64
Sample 0.28
0.30
0.14
0.28
Biases in parameter estimates
Biases in estimated variances
Weighted log likelihood, weight =
Fixup of covariance matrix
j / Fj
for all i.
; Choices = list of names / list of true proportions $
; Choices = Air,Train,Bus,Car / 0.14, 0.13, 0.09, 0.64
Choice Based Sampling Estimators
--------+-------------------------------------------------Variable| Coefficient
Standard Error b/St.Er. P[|Z|>z]
--------+-------------------------------------------------Unweighted
TTME|
-.10289***
.01109
-9.280
.0000
INVC|
-.08044***
.01995
-4.032
.0001
INVT|
-.01399***
.00267
-5.240
.0000
GC|
.07578***
.01833
4.134
.0000
A_AIR|
4.37035***
1.05734
4.133
.0000
AIR_HIN1|
.00428
.01306
.327
.7434
A_TRAIN|
5.91407***
.68993
8.572
.0000
TRA_HIN2|
-.05907***
.01471
-4.016
.0001
A_BUS|
4.46269***
.72333
6.170
.0000
BUS_HIN3|
-.02295
.01592
-1.442
.1493
--------+-------------------------------------------------Weighted
TTME|
-.13611***
.02538
-5.363
.0000
INVC|
-.10351***
.02470
-4.190
.0000
INVT|
-.01772***
.00323
-5.486
.0000
GC|
.10225***
.02107
4.853
.0000
A_AIR|
4.52505***
1.75589
2.577
.0100
AIR_HIN1|
.00746
.01481
.504
.6145
A_TRAIN|
5.53229***
.97331
5.684
.0000
TRA_HIN2|
-.06026***
.02235
-2.696
.0070
A_BUS|
4.36579***
.97182
4.492
.0000
BUS_HIN3|
-.01957
.01631
-1.200
.2302
Changes in Estimated Elasticities
+---------------------------------------------------+
| Unweighted
|
| Elasticity
averaged over observations.|
| Attribute is INVC
in choice CAR
|
| Effects on probabilities of all choices in model: |
| * = Direct Elasticity effect of the attribute.
|
|
Mean
St.Dev
|
|
Choice=AIR
.3622
.3437
|
|
Choice=TRAIN
.3622
.3437
|
|
Choice=BUS
.3622
.3437
|
| *
Choice=CAR
-1.3266
1.1731
|
+---------------------------------------------------+
| Weighted
|
| Elasticity
averaged over observations.|
| Attribute is INVC
in choice CAR
|
| Effects on probabilities of all choices in model: |
| * = Direct Elasticity effect of the attribute.
|
|
Mean
St.Dev
|
|
Choice=AIR
.8371
.7363
|
|
Choice=TRAIN
.8371
.7363
|
|
Choice=BUS
.8371
.7363
|
| *
Choice=CAR
-1.3362
1.4557
|
+---------------------------------------------------+
Testing IIA vs. AIR Choice
? No alternative constants in the model
NLOGIT
NLOGIT
; Lhs = Mode
; Choices = Air,Train,Bus,Car
; Rhs = TTME,INVC,INVT,GC$
; Lhs = Mode
; Choices = Air,Train,Bus,Car
; Rhs = TTME,INVC,INVT,GC
; IAS = Air $
Testing IIA – Dealing with Constants
With ASCs in the model, the covariance matrix becomes singular
because the constant for AIR is always zero within the reduced
sample. Do the test against the other coefficients.
NLOGIT ; Lhs = Mode
; Choices = Air,Train,Bus,Car
; Rhs = TTME,INVC,INVT,GC,One$
MATRIX ; Bair = b(1:4) ; Vair = Varb(1:4,1:4) $
NLOGIT ; Lhs = Mode
; Choices = Air,Train,Bus,Car
; Rhs = TTME,INVC,INVT,GC,One
; IAS = Air$
MATRIX ; BNoair=b(1:4) ; VNoair = Varb(1:4,1:4) $
MATRIX ; Db = BNoair-BAir ; Dv = VNoair - Vair $
MATRIX ; List ; H = Db'<Dv>Db $
Lab Session 8
Part 2
Nested Logit Models
Extensions of the MNL
Using NLOGIT To Fit the Model
Start program
Load CLOGIT.LPJ project
Specify trees with
:TREE = name1(alt1,alt2…),
name2(alt…. ),…
“Names” are optional names for branches.
Nested Logit Model
? Load the CLOGIT data
?
? (1) A simple nested logit model
?
NLOGIT ; Lhs = Mode
; RHS = GC, TTME, INVT ; RH2 = ONE
; Choices = Air,Train,Bus,Car
; Tree = Private (Air,Car) , Public (Train,Bus) $
Model Form RU1
Twig Level Probability
exp(β'x k|j )
Prob(Choice = k | j) =

K|j
m=1
exp(β'x m|j )
Inclusive Value for the Branch
K|j
IV(j) = log   m=1 exp(β'x m|j )


Branch Probability
Prob(Branch = j)=

exp  λ j  γ'y j +IV(j)  
B
b=1
exp  λb  γ'yb +IV(b)  
λ j = 1 Returns the Multinomial Logit Model
Moving Scaling Down to the Twig Level
RU2 Normalization (;RU2)
 βx k|j 
exp 

μ
 j 
Twig Level Probability : Pk|j 
 βx m|j 
k|j
 m=1 exp  μ 
 j 
 k|j
 βx m|j  
Inclusive Value for the Branch : IV(j) = log   m=1 exp 
 

 μj  

Branch Probability : Pj 

exp  γy j  μjIV(j)
B
b=1
exp  γyb +μbIV(b)
Normalizations
There are different ways to normalize the
variances in the nested logit model, at the
lowest level, or up at the highest level. Use
;RU1 for the low level
or
;RU2 to normalize at the branch level
Normalizations of Nested Logit Models
?
? (2) Renormalize the nested logit model
?
NLOGIT ; Lhs = Mode ; RHS = GC, TTME, INVT
; RH2 = ONE
; Choices = Air,Train,Bus,Car
; Tree = Private (Air,Car) , Public (Train,Bus)
; RU1 $
NLOGIT ; Lhs = Mode ; RHS = GC, TTME, INVT
; RH2 = ONE
; Choices = Air,Train,Bus,Car
; Tree = Private (Air,Car) , Public (Train,Bus)
; RU2 $
Fixing IV Parameters
With branches defined by
;TREE = br1(…),br2(…),…,brK(…)
(a) Force IV parameters to be equal with
; IVSET: (br1,…) The list may contain
any or all of the branch names
(b) Force IV parameters to equal specific
values
; IVSET: (br1,…) = [ the value ]
Constraining the IV Parameters
? (3) Force the IV parameters to be equal
NLOGIT ; Lhs = Mode ; RHS = GC, TTME, INVT
; RH2 = ONE
; Choices = Air,Train,Bus,Car
; Tree = Private (Air,Car) , Public (Train,Bus)
; RU2 ; IVSET: (Private,Public) $
NLOGIT ; Lhs = Mode ; RHS = GC, TTME, INVT
; RH2 = ONE
; Choices = Air,Train,Bus,Car
; Tree = Private (Air,Car) , Public (Train,Bus)
; RU2 ; IVSET: (Private,Public) = [1] $
? The preceding constraint produces the simple MNL model
NLOGIT ; Lhs = Mode ; RHS = GC, TTME, INVT
; RH2 = ONE
; Choices = Air,Train,Bus,Car $
Degenerate Branch
? (4) Fit the model with a degenerate branch
NLOGIT ; Lhs = Mode ; RHS = GC, TTME, INVT
; RH2 = ONE
; Choices = Air,Train,Bus,Car
; Tree = Fly (Air) , Ground (Train,Bus,Car) $
? (5) Study scaling differences with nested logit rather
?
than HEV. Make all alts their own branch. One is
?
normalized to 1.000.
NLOGIT ; Lhs = Mode ; RHS = GC, TTME, INVT
; RH2 = ONE
; Choices = Air,Train,Bus,Car
; Tree = Fly(Air),Rail(Train), Autobus(Bus),Auto(Car)
; IVSET: (Fly) = [1] $
Heteroscedasticity in the MNL Model
Add ;HET to the generic NLOGIT
command. No other changes.
NLOGIT
; Lhs = Mode
; Choices = Air,Train,Bus,Car
; Rhs = TTME,INVC,INVT,GC,One
; Het
; Effects: INVT(*) $
Heteroscedastic Extreme Value Model (1)
----------------------------------------------------------Start values obtained using MNL model
Dependent variable
Choice
Log likelihood function
-184.50669
Estimation based on N =
210, K =
7
Information Criteria: Normalization=1/N
Normalized
Unnormalized
AIC
1.82387
383.01339
Fin.Smpl.AIC
1.82651
383.56784
Bayes IC
1.93544
406.44314
Hannan Quinn
1.86898
392.48517
R2=1-LogL/LogL* Log-L fncn R-sqrd R2Adj
Constants only
-283.7588 .3498 .3393
Chi-squared[ 4]
=
198.50415
Prob [ chi squared > value ] =
.00000
Response data are given as ind. choices
Number of obs.=
210, skipped
0 obs
--------+-------------------------------------------------Variable| Coefficient
Standard Error b/St.Er. P[|Z|>z]
--------+-------------------------------------------------TTME|
-.10365***
.01094
-9.476
.0000
INVC|
-.08493***
.01938
-4.382
.0000
INVT|
-.01333***
.00252
-5.297
.0000
GC|
.06930***
.01743
3.975
.0001
A_AIR|
5.20474***
.90521
5.750
.0000
A_TRAIN|
4.36060***
.51067
8.539
.0000
A_BUS|
3.76323***
.50626
7.433
.0000
--------+--------------------------------------------------
Heteroscedastic Extreme Value Model (2)
----------------------------------------------------------Heteroskedastic Extreme Value Model
Dependent variable
MODE
Use to test vs. IIA assumption in
Log likelihood function
-182.44396
model? LogL0 = -184.5067.
Restricted log likelihood
-291.12182
Chi squared [ 10 d.f.]
217.35572
R2=1-LogL/LogL* Log-L fncn R-sqrd R2Adj
IIA would not be rejected on this
No coefficients -291.1218 .3733 .3632
(Not necessarily a test of that
Constants only
-283.7588 .3570 .3467
methodological assumption.)
At start values -218.6505 .1656 .1521
Response data are given as ind. choices
Number of obs.=
210, skipped
0 obs
--------+-------------------------------------------------Variable| Coefficient
Standard Error b/St.Er. P[|Z|>z]
--------+-------------------------------------------------|Attributes in the Utility Functions (beta)
TTME|
-.11526**
.05721
-2.014
.0440
INVC|
-.15516*
.07928
-1.957
.0503
INVT|
-.02277**
.01123
-2.028
.0426
GC|
.11904*
.06403
1.859
.0630
A_AIR|
4.69411*
2.48092
1.892
.0585
A_TRAIN|
5.15630**
2.05744
2.506
.0122
A_BUS|
5.03047**
1.98259
2.537
.0112
|Scale Parameters of Extreme Value Distns Minus 1.
s_AIR|
-.57864***
.21992
-2.631
.0085
s_TRAIN|
-.45879
.34971
-1.312
.1896
s_BUS|
.26095
.94583
.276
.7826
s_CAR|
.000
......(Fixed Parameter)......
|Std.Dev=pi/(theta*sqr(6)) for H.E.V. distribution
s_AIR|
3.04385*
1.58867
1.916
.0554
s_TRAIN|
2.36976
1.53124
1.548
.1217
s_BUS|
1.01713
.76294
1.333
.1825
s_CAR|
1.28255
......(Fixed Parameter)......
--------+--------------------------------------------------
Normalized for estimation
Structural parameters
MNL
basis.
HEV Model - Elasticities
+---------------------------------------------------+
| Elasticity
averaged over observations.|
| Attribute is INVC
in choice AIR
|
| Effects on probabilities of all choices in model: |
| * = Direct Elasticity effect of the attribute.
|
|
Mean
St.Dev
|
| *
Choice=AIR
-4.2604
1.6745
|
|
Choice=TRAIN
1.5828
1.9918
|
|
Choice=BUS
3.2158
4.4589
|
|
Choice=CAR
2.6644
4.0479
|
| Attribute is INVC
in choice TRAIN
|
|
Choice=AIR
.7306
.5171
|
| *
Choice=TRAIN
-3.6725
4.2167
|
|
Choice=BUS
2.4322
2.9464
|
|
Choice=CAR
1.6659
1.3707
|
| Attribute is INVC
in choice BUS
|
|
Choice=AIR
.3698
.5522
|
|
Choice=TRAIN
.5949
1.5410
|
| *
Choice=BUS
-6.5309
5.0374
|
|
Choice=CAR
2.1039
8.8085
|
| Attribute is INVC
in choice CAR
|
|
Choice=AIR
.3401
.3078
|
|
Choice=TRAIN
.4681
.4794
|
|
Choice=BUS
1.4723
1.6322
|
| *
Choice=CAR
-3.5584
9.3057
|
+---------------------------------------------------+
Multinomial Logit
+---------------------------+
| INVC
in AIR
|
|
Mean
St.Dev
|
| *
-5.0216
2.3881
|
|
2.2191
2.6025
|
|
2.2191
2.6025
|
|
2.2191
2.6025
|
| INVC
in TRAIN
|
|
1.0066
.8801
|
| *
-3.3536
2.4168
|
|
1.0066
.8801
|
|
1.0066
.8801
|
| INVC
in BUS
|
|
.4057
.6339
|
|
.4057
.6339
|
| *
-2.4359
1.1237
|
|
.4057
.6339
|
| INVC
in CAR
|
|
.3944
.3589
|
|
.3944
.3589
|
|
.3944
.3589
|
| *
-1.3888
1.2161
|
+---------------------------+
Heterogeneous HEV Model
Does the variance depend on
household income?
NLOGIT
; Lhs = Mode
; Choices = Air,Train,Bus,Car
; Rhs = TTME,INVC,INVT,GC,One
; Het ; Hfn = HINC
; Effects: INVT(*) $
Lab Session 9
Multinomial Probit
Mixed Logit (Random Parameters)
Latent Class Models
Multinomial Probit Model

Add ;MNP to the generic command

Use ;PTS=number to specify the number of
points in the simulations. Use a small
number (15) for demonstrations and
examples. Use a large number (200+) for
real estimation.

(Don’t fit this now. Takes forever to
compute. Much less practical – and probably
less useful – than other specifications.)
Multinomial Probit Model
--------+-------------------------------------------------Variable| Coefficient
Standard Error b/St.Er. P[|Z|>z]
--------+-------------------------------------------------|Attributes in the Utility Functions (beta)
GC|
.11825**
.04783
2.472
.0134
TTME|
-.09105***
.03439
-2.647
.0081
INVC|
-.14880***
.05495
-2.708
.0068
INVT|
-.02300***
.00797
-2.886
.0039
A_AIR|
2.94413*
1.59671
1.844
.0652
A_TRAIN|
4.64736***
1.50865
3.080
.0021
A_BUS|
4.09869***
1.29880
3.156
.0016
|Std. Devs. of the Normal Distribution.
s[AIR]|
3.99782**
1.59304
2.510
.0121
s[TRAIN]|
1.63224*
.86143
1.895
.0581
s[BUS]|
1.00000
......(Fixed Parameter)......
s[CAR]|
1.00000
......(Fixed Parameter)......
|Correlations in the Normal Distribution
rAIR,TRA|
.31999
.53343
.600
.5486
rAIR,BUS|
.40675
.70841
.574
.5659
rTRA,BUS|
.37434
.41343
.905
.3652
rAIR,CAR|
.000
......(Fixed Parameter)......
rTRA,CAR|
.000
......(Fixed Parameter)......
rBUS,CAR|
.000
......(Fixed Parameter)......
--------+--------------------------------------------------
MNP Elasticities
+---------------------------------------------------+
| Elasticity
averaged over observations.|
| Attribute is INVT
in choice AIR
|
| Effects on probabilities of all choices in model: |
| * = Direct Elasticity effect of the attribute.
|
|
Mean
St.Dev
|
| *
Choice=AIR
-1.0154
.4600
|
|
Choice=TRAIN
.4773
.4052
|
|
Choice=BUS
.6124
.4282
|
|
Choice=CAR
.3237
.3037
|
+---------------------------------------------------+
| Attribute is INVT
in choice TRAIN
|
|
Choice=AIR
1.8113
1.6718
|
| *
Choice=TRAIN
-11.8375
10.1346
|
|
Choice=BUS
7.9668
6.8088
|
|
Choice=CAR
4.3257
4.4078
|
+---------------------------------------------------+
| Attribute is INVT
in choice BUS
|
|
Choice=AIR
.9635
1.4635
|
|
Choice=TRAIN
3.9555
6.7724
|
| *
Choice=BUS
-23.3467
14.2837
|
|
Choice=CAR
4.6840
7.8314
|
+---------------------------------------------------+
| Attribute is INVT
in choice CAR
|
|
Choice=AIR
1.3324
1.4476
|
|
Choice=TRAIN
4.5062
4.7695
|
|
Choice=BUS
9.6001
7.6406
|
| *
Choice=CAR
-10.8870
10.0449
|
+---------------------------------------------------+
Data Sets for Random Parameters Modeling
(1) clogit.lpj (as before)
(2) brandchoicesSP.LPJ is 8 choice situations per person, 4 choices.
True underlying model is a three class latent class model
(3) panelprobit.lpj is 5 binary outcome situations per firm, 1270
firms. This has only firm specific data, no “choice specific”
data. Suitable for Random Parameters Probit Models
(4) innovation.lpj is 5 “choice” situations per firm. Converted the
panel probit.lpj data to a format amenable to the RPL program
in NLOGIT. Second line of each outcome is the other outcome,
“not innovate” plus zeros for the “attributes.”
(5) healthcare.lpj is a panel data set with numerous variables
(DocVis, HospVis, DOCTOR, HOSPITAL, HSAT) that can be
modeled with random parameters models. There are varying
numbers of observations per person.
(6) sprp.lpj is a mixed revealed/stated multinomial choice data set. There
are a mixture of a variable number of choices per person as well as a
choice among the elements of a master choice set.
Panel Data Formats
In case (1)
; PDS = 1
(2) use ; PDS = 8
(3)
; PDS = 5
(4)
; PDS = 5
(5)
; PDS = _Groupti
(6)
; PDS = 4
(See discussion in Lab Session 10)
Commands for Random Parameters
Model name
; Lhs = …
; Rhs = …
; … < any other specifications >
; RPM if not NLOGIT or ;RPL if NLOGIT model
; PTS = the number of points (use 25 for our class)
; PDS = the panel data spedification
; Halton (to get better results)
; FCN = the specification of the random parameters $
Random Parameter Specifications
All models in LIMDEP/NLOGIT may be fit with random parameters,
with panel or cross sections. NLOGIT has more options (not
shown here) than the more general cases.
Options for specifications
; Correlated parameters (otherwise, independent)
; FCN = name ( type ).
Type is N = normal,
U = uniform,
L = lognormal (positive),
T = tent shaped distributions.
C = nonrandom (variance = 0 – only in NLOGIT)
Name is the name of a variable or parameter in the model or
A_choice for ASCs (up to 8 characters). In the CLOGIT
model, they are A_AIR A_TRAIN A_BUS.
Replicability
Consecutive runs of the identical model give
different results. Why? Different random draws.
Achieve replicability


Use ;HALTON
Set random number generator before each
run with the same value.
CALC ; Ran( large odd number) $
Random Parameters Models
PROBIT
; Lhs = IP
; Rhs = One,IMUM,FDIUM,LogSales
; RPM ; Pts = 25 ; Halton ; Pds = 5
; Fcn = IMUM(N),FDIUM(N) ; Correlated $
POISSON
; Lhs = Doctor
; Rhs = One,Educ,Age,Hhninc,Hhkids
; Fcn = Educ(N)
; Pds=_Groupti ; Pts=100 ; Halton
; Maxit = 25 $
And so on…
Random Effects in Utility Functions
Model has
U(i,j,t) = ’x(i,j,t) + e(i,j,t) + w(i,j)
w(i,j) is constant across time, correlated across utilities
RPLogit ;
;
;
;
;
;
lhs=mode ; choices=air,train,bus,car
rhs=gc,ttme
rh2=one
rpl ; maxit=50;pts=25;halton ; pds=5
fcn=a_air(n),a_train(n),a_bus(n)
Correlated $
Random Effects in Utility Functions
Model has
U(i,j,t) = ’x(i,j,t) + e(i,j,t) + w(i,m)
w(i,m) is constant across time, the same for specified
groups of utilities.
? This specifies two effects, one for private, one for
public
ECLogit ; lhs=mode
; choices=air,train,bus,car
; rhs=gc,ttme
; rh2=one
; rpl ; maxit=50;pts=25;halton ; pds=5
; fcn=a_air(n),a_train(n),a_bus(n)
; ECM= (air,car),(bus,car) $
Options for Random Parameters in NLOGIT Only








Name ( type ) = as described above
Name ( C ) = a constant parameter. Variance = 0
Name (T,*) = triangular with one end at 0 the other at 2
Name (type | value) = fixes the mean at value, variance is free
Name (type | # ) if variables in RPL=list, they do not apply to this
parameter. Mean is constant.
Name (type | #pattern) as above, but pattern is used to remove only
some variables in RPL=list. Pattern is 1s and 0s. E.g., if
RPL=Hinc,Psize, GC(N | #10) allows only Hinc in the mean.
Name (type , value ) = forces standard deviation to equal value
times absolute value of .
Name (type,*,value) forces mean equal to value, variance is free,
any variables in RPL=list are removed for this parameter.
Some Random Parameters Models
? Basic random parameters model
Nlogit
; lhs=mode
; choices=air,train,bus,car
; rhs=gc,ttme,invt ; rh2=one
; rpl ; maxit=50 ;pts=25 ; halton ; pds=5
; fcn=gc(n),ttme(n),invt(n) $
?
? Random parameters model with constrained parameter.
Nlogit
; lhs=mode
; choices=air,train,bus,car
; rhs=gc,ttme,invt ; rh2=one
; rpl ; maxit=50 ;pts=25 ; halton ; pds=5
; fcn=gc(t,*),ttme(n),invt(n) $
?
? Random parameters with effects to induce correlation
Nlogit
; lhs=mode
; choices=air,train,bus,car
; rhs=gc,ttme,invt ; rh2=one
; rpl ; maxit=50 ;pts=25 ; halton ; pds=5
; fcn=gc(n),ttme(n),invt(n)
; kernel = (air,car),(bus,train) $
Constructed Parameters with Restrictions
? Dummy variables for PUBLIC or PRIVATE mode
Create ; apriv = aasc + casc ; apub = tasc + basc$
? Model contains a “type” effect (random effect) in the
? Utility functions. Note, no coefficients, just random variation.
Nlogit
; lhs=mode ; choices=air,train,bus,car
; rhs=gc,ttme,apriv,apub
; rh2=one
; rpl ; maxit=50;pts=25;halton;output=3; pds=5
; fcn=apriv(n,*,0), apub(n,*,0) $
Using NLOGIT To Fit an LC Model
Start program
Load BrandChoices.lpj project
This is the artificial shoe brand choice data.
Specify the model with
; LCM ; PTS = number of classes
To request class probabilities to depend on
variables in the data, use
; LCM = the variables
(Do not include ONE in this variables list.)
Latent Choice Models
? Load the BrandChoicesSP.lpj data set.
(1) Three class model. (The truth)
NLOGIT ;Lhs=choice
;Choices=Brand1,Brand2,Brand3,None
;Rhs = Fash,Qual,Price,ASC4
;lcm;pds=8 ;pts=3 ;Crosstab $
(2) Try with different numbers of classes
NLOGIT ;Lhs=choice
;Choices=Brand1,Brand2,Brand3,None
;Rhs = Fash,Qual,Price,ASC4
;lcm;pds=8 ;pts=2 ;Crosstab $
NLOGIT ;Lhs=choice
;Choices=Brand1,Brand2,Brand3,None
;Rhs = Fash,Qual,Price,ASC4
;lcm;pds=8 ;pts=4 ;Crosstab $
Latent Class Models
(3) More elaborate model for class probabilities
NLOGIT ;Lhs=choice
;Choices=Brand1,Brand2,Brand3,None
;Rhs = Fash,Qual,Price,ASC4
;lcm=Male,Agel25,Age2539 ;pds=8 ;pts=4
;Crosstab $
(4) Compare LCM to a simpler model - Nested Logit
NLOGIT ;Lhs=choice
;Choices=Brand1,Brand2,Brand3,None
;Rhs = Fash,Qual,Price,ASC4
;Tree=Shoes(brand*),NoShoes(none)
;ivset:(noshoes)=[1]
;Crosstab $
(5) Try some other experiments
Lab Session 10
Discrete Choice
Combining RP and SP Data
Application
Survey sample of 2,688 trips, 2 or 4 choices per situation
Sample consists of 672 individuals
Choice based sample
Revealed/Stated choice experiment:
Revealed: Drive,ShortRail,Bus,Train
Hypothetical: Drive,ShortRail,Bus,Train,LightRail,ExpressBus
Attributes:
Cost –Fuel or fare
Transit time
Parking cost
Access and Egress time
Data Set
Load data set RPSP.LPJ
9408 observations
We fit separate models for RP and
SP subsets of the data, then a
combined, nested model that
accommodates the different
scaling.
Each person makes four choices
from a choice set that includes either
two or four alternatives.
The first choice is the RP between
two of the RP alternatives
The second-fourth are the SP among
four of the six SP alternatives.
There are ten alternatives in total.
Model for Revealed Preference Data
? Using only Revealed Preference Data
sample;all$
reject;sprp=2$ deleting SP data
dstats;rhs=autotime,fcost,mptrtime,mptrfare$
NLOGIT
;lhs=chosen,cset,altij
;choices=RPDA,RPRS,RPBS,RPTN
;descriptives;crosstab
;maxit=100
;model:
U(RPDA) = rdasc+ fl*fcost+tm*autotime/
U(RPRS) = rrsasc+ fl*fcost+tm*autotime/
U(RPBS) = rbsasc + ptc*mptrfare+mt*mptrtime/
U(RPTN) =
ptc*mptrfare+mt*mptrtime$
Model for Stated Preference Data
? Using only Stated Preference Data
sample;all$
reject;sprp=1$ deleting RP data
? BASE MODEL
nlogit
;lhs=chosen,cset,alt
;choices=SPDA,SPRS,SPBS,SPTN,SPLR,SPBW
;descriptives;crosstab
;maxit=150
;model:
U(SPDA) = dasc +cst*fueld+ tmcar*time+prk*parking
+pincda*pincome +cavda*carav/
U(SPRS) = rsasc+cst*fueld+ tmcar*time+prk*parking/
U(SPBS) = bsasc+cst*fared+ tmpt*time+act*acctime+egt*eggtime/
U(SPTN) = tnasc+cst*fared+ tmpt*time+act*acctime+egt*eggtime/
U(SPLR) = lrasc+cst*fared+ tmpt*time+act*acctime +egt*eggtime/
U(SPBW) =
cst*fared+ tmpt*time+act*acctime+egt*eggtime$
A Nested Logit Model for RP/SP Data
NLOGIT
;lhs=chosen,cset,altij
;choices=RPDA,RPRS,RPBS,RPTN,SPDA,SPRS,SPBS,SPTN,SPLR,SPBW
/.592,.208,.089,.111,1.0,1.0,1.0,1.0,1.0,1.0
;tree=mode[rp(RPDA,RPRS,RPBS,RPTN),spda(SPDA),
sprs(SPRS),spbs(SPBS),sptn(SPTN),splr(SPLR),spbw(SPBW)]
;ivset: (rp)=[1.0];ru1
;maxit=150
;model:
U(RPDA) = rdasc+ invc*fcost+tmrs*autotime ?+prkda*vehprkct+
+ pinc*pincome+CAVDA*CARAV/
U(RPRS) = rrsasc + invc*fcost+tmrs*autotime/?+
U(RPBS) = rbsasc + invc*mptrfare+mtpt*mptrtime/?+acegt*rpacegtm/
U(RPTN) =
cstrs*mptrfare+mtpt*mptrtime/?+acegt*rpacegtm/
U(SPDA) = sdasc + invc*fueld + tmrs*time+cavda*carav ?+prkda*parking
+ pinc*pincome/
U(SPRS) = srsasc + invc*fueld + tmrs*time/? cavrs*carav/
U(SPBS) =
invc*fared + mtpt*time +acegt*spacegtm/
U(SPTN) = stnasc + invc*fared + mtpt*time+acegt*spacegtm/
U(SPLR) = slrasc + invc*fared + mtpt*time+acegt*spacegtm/
U(SPBW) = sbwasc + invc*fared + mtpt*time+acegt*spacegtm$
A Random Parameters Approach
NLOGIT
;lhs=chosen,cset,altij
;choices=RPDA,RPRS,RPBS,RPTN,SPDA,SPRS,SPBS,SPTN,SPLR,SPBW
/.592,.208,.089,.111,1.0,1.0,1.0,1.0,1.0,1.0
; rpl
; pds=4
; halton ; pts=25
; fcn=invc(n)
; model:
U(RPDA) = rdasc+ invc*fcost+tmrs*autotime ?+prkda*vehprkct+
+ pinc*pincome+CAVDA*CARAV/
U(RPRS) = rrsasc + invc*fcost+tmrs*autotime/?+
?egt*autoegtm+prk*vehprkct+
U(RPBS) = rbsasc + invc*mptrfare+mtpt*mptrtime/?+acegt*rpacegtm/
U(RPTN) =
cstrs*mptrfare+mtpt*mptrtime/?+acegt*rpacegtm/
U(SPDA) = sdasc + invc*fueld + tmrs*time+cavda*carav
?+prkda*parking
+ pinc*pincome/
U(SPRS) = srsasc + invc*fueld + tmrs*time/? cavrs*carav/
U(SPBS) =
invc*fared + mtpt*time +acegt*spacegtm/
U(SPTN) = stnasc + invc*fared + mtpt*time+acegt*spacegtm/
U(SPLR) = slrasc + invc*fared + mtpt*time+acegt*spacegtm/
U(SPBW) = sbwasc + invc*fared + mtpt*time+acegt*spacegtm$
Connecting Choice
Situations through RPs
--------+-------------------------------------------------Variable| Coefficient
Standard Error b/St.Er. P[|Z|>z]
--------+-------------------------------------------------|Random parameters in utility functions
INVC|
-.58944***
.03922
-15.028
.0000
|Nonrandom parameters in utility functions
RDASC|
-.75327
.56534
-1.332
.1827
TMRS|
-.05443***
.00789
-6.902
.0000
PINC|
.00482
.00451
1.068
.2857
CAVDA|
.35750***
.13103
2.728
.0064
RRSASC|
-2.18901***
.54995
-3.980
.0001
RBSASC|
-1.90658***
.53953
-3.534
.0004
MTPT|
-.04884***
.00741
-6.591
.0000
CSTRS|
-1.57564***
.23695
-6.650
.0000
SDASC|
-.13612
.27616
-.493
.6221
SRSASC|
-.10172
.18943
-.537
.5913
ACEGT|
-.02943***
.00384
-7.663
.0000
STNASC|
.13402
.11475
1.168
.2428
SLRASC|
.27250**
.11017
2.473
.0134
SBWASC|
-.00685
.09861
-.070
.9446
|Distns. of RPs. Std.Devs or limits of triangular
NsINVC|
.45285***
.05615
8.064
.0000
--------+--------------------------------------------------